Main Strategies for the Identification of Neoantigens

Real-time detection of 20 amino acids and discrimination of pathologically relevant peptides with functionalized nanopore

Precise identification and quantification of amino acids is crucial for many biological applications. Here we report a copper(II)-functionalized Mycobacterium smegmatis porin A (MspA) nanopore with the N91H substitution, which enables direct identification of all 20 proteinogenic amino acids when combined with a machine-learning algorithm. The validation accuracy reaches 99.1%, with 30.9% signal recovery. The feasibility of ultrasensitive quantification of amino acids was also demonstrated at the nanomolar range. Furthermore, the capability of this system for real-time analyses of two representative post-translational modifications (PTMs), one unnatural amino acid and ten synthetic peptides using exopeptidases, including clinically relevant peptides associated with Alzheimer's disease and cancer neoantigens, was demonstrated. Notably, our strategy successfully distinguishes peptides with only one amino acid difference from the hydrolysate and provides the possibility to infer the peptide sequence.

Hyperthermia in Combination with Emerging Targeted and Immunotherapies as a New Approach in Cancer Treatment

Despite significant advancements in the development of novel therapies, cancer continues to stand as a prominent global cause of death. In many cases, the cornerstone of standard-of-care therapy consists of chemotherapy (CT), radiotherapy (RT), or a combination of both. Notably, hyperthermia (HT), which has been in clinical use in the last four decades, has proven to enhance the effectiveness of CT and RT, owing to its recognized potency as a sensitizer. Furthermore, HT exerts effects on all steps of the cancer-immunity cycle and exerts a significant impact on key oncogenic pathways. Most recently, there has been a noticeable expansion of cancer research related to treatment options involving immunotherapy (IT) and targeted therapy (TT), a trend also visible in the research and development pipelines of pharmaceutical companies. However, the potential results arising from the combination of these innovative therapeutic approaches with HT remain largely unexplored. Therefore, this review aims to explore the oncology pipelines of major pharmaceutical companies, with the primary objective of identifying the principal targets of forthcoming therapies that have the potential to be advantageous for patients by specifically targeting molecular pathways involved in HT. The ultimate goal of this review is to pave the way for future research initiatives and clinical trials that harness the synergy between emerging IT and TT medications when used in conjunction with HT.

Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy

Advancements in sequencing technologies and bioinformatics algorithms have expanded our ability to identify tumor-specific somatic mutation-derived antigens (neoantigens). While recent studies have shown neoantigens to be compelling targets for cancer immunotherapy due to their foreign nature and high immunogenicity, the need for increasingly accurate and cost-effective approaches to rapidly identify neoantigens remains a challenging task, but essential for successful cancer immunotherapy. Currently, gene expression analysis and algorithms for variant calling can be used to generate lists of mutational profiles across patients, but more care is needed to curate these lists and prioritize the candidate neoantigens most capable of inducing an immune response. A growing amount of evidence suggests that only a handful of somatic mutations predicted by mutational profiling approaches act as immunogenic neoantigens. Hence, unbiased screening of all candidate neoantigens predicted by Whole Genome Sequencing/Whole Exome Sequencing may be necessary to more comprehensively access the full spectrum of immunogenic neoepitopes. Once putative cancer neoantigens are identified, one of the largest bottlenecks in translating these neoantigens into actionable targets for cell-based therapies is identifying the cognate T cell receptors (TCRs) capable of recognizing these neoantigens. While many TCR-directed screening and validation assays have utilized bulk samples in the past, there has been a recent surge in the number of single-cell assays that provide a more granular understanding of the factors governing TCR-pMHC interactions. The goal of this review is to provide an overview of existing strategies to identify candidate neoantigens using genomics-based approaches and methods for assessing neoantigen immunogenicity. Additionally, applications, prospects, and limitations of some of the current single-cell technologies will be discussed. Finally, we will briefly summarize some of the recent models that have been used to predict TCR antigen specificity and analyze the TCR receptor repertoire.

Structural basis for T cell recognition of cancer neoantigens and implications for predicting neoepitope immunogenicity

Adoptive cell therapy (ACT) with tumor-specific T cells has been shown to mediate durable cancer regression. Tumor-specific T cells are also the basis of other therapies, notably cancer vaccines. The main target of tumor-specific T cells are neoantigens resulting from mutations in self-antigens over the course of malignant transformation. The detection of neoantigens presents a major challenge to T cells because of their high structural similarity to self-antigens, and the need to avoid autoimmunity. How different a neoantigen must be from its wild-type parent for it to induce a T cell response is poorly understood. Here we review recent structural and biophysical studies of T cell receptor (TCR) recognition of shared cancer neoantigens derived from oncogenes, including p53R175H, KRASG12D, KRASG12V, HHATp8F, and PIK3CAH1047L. These studies have revealed that, in some cases, the oncogenic mutation improves antigen presentation by strengthening peptide-MHC binding. In other cases, the mutation is detected by direct interactions with TCR, or by energetically driven or other indirect strategies not requiring direct TCR contacts with the mutation. We also review antibodies designed to recognize peptide-MHC on cell surfaces (TCR-mimic antibodies) as an alternative to TCRs for targeting cancer neoantigens. Finally, we review recent computational advances in this area, including efforts to predict neoepitope immunogenicity and how these efforts may be advanced by structural information on peptide-MHC binding and peptide-MHC recognition by TCRs.

Personalised neoantigen-based therapy in colorectal cancer

Colorectal cancer (CRC) has become one of the most common tumours with high morbidity, mortality and distinctive evolution mechanism. The neoantigens arising from the somatic mutations have become considerable treatment targets in the management of CRC. As cancer-specific aberrant peptides, neoantigens can trigger the robust host immune response and exert anti-tumour effects while minimising the emergence of adverse events commonly associated with alternative therapeutic regimens. In this review, we summarised the mechanism, generation, identification and prognostic significance of neoantigens, as well as therapeutic strategies challenges of neoantigen-based therapy in CRC. The evidence suggests that the establishment of personalised neoantigen-based therapy holds great promise as an effective treatment approach for patients with CRC.

Tutorial: integrative computational analysis of bulk RNA-sequencing data to characterize tumor immunity using RIMA

RNA-sequencing (RNA-seq) has become an increasingly cost-effective technique for molecular profiling and immune characterization of tumors. In the past decade, many computational tools have been developed to characterize tumor immunity from gene expression data. However, the analysis of large-scale RNA-seq data requires bioinformatics proficiency, large computational resources and cancer genomics and immunology knowledge. In this tutorial, we provide an overview of computational analysis of bulk RNA-seq data for immune characterization of tumors and introduce commonly used computational tools with relevance to cancer immunology and immunotherapy. These tools have diverse functions such as evaluation of expression signatures, estimation of immune infiltration, inference of the immune repertoire, prediction of immunotherapy response, neoantigen detection and microbiome quantification. We describe the RNA-seq IMmune Analysis (RIMA) pipeline integrating many of these tools to streamline RNA-seq analysis. We also developed a comprehensive and user-friendly guide in the form of a GitBook with text and video demos to assist users in analyzing bulk RNA-seq data for immune characterization at both individual sample and cohort levels by using RIMA.

The use of RNA-based treatments in the field of cancer immunotherapy

Over the past several decades, mRNA vaccines have evolved from a theoretical concept to a clinical reality. These vaccines offer several advantages over traditional vaccine techniques, including their high potency, rapid development, low-cost manufacturing, and safe administration. However, until recently, concerns over the instability and inefficient distribution of mRNA in vivo have limited their utility. Fortunately, recent technological advancements have mostly resolved these concerns, resulting in the development of numerous mRNA vaccination platforms for infectious diseases and various types of cancer. These platforms have shown promising outcomes in both animal models and humans. This study highlights the potential of mRNA vaccines as a promising alternative approach to conventional vaccine techniques and cancer treatment. This review article aims to provide a thorough and detailed examination of mRNA vaccines, including their mechanisms of action and potential applications in cancer immunotherapy. Additionally, the article will analyze the current state of mRNA vaccine technology and highlight future directions for the development and implementation of this promising vaccine platform as a mainstream therapeutic option. The review will also discuss potential challenges and limitations of mRNA vaccines, such as their stability and in vivo distribution, and suggest ways to overcome these issues. By providing a comprehensive overview and critical analysis of mRNA vaccines, this review aims to contribute to the advancement of this innovative approach to cancer treatment.

Assessing TCR identity, knock-in efficiency, and potency for individualized TCR-T cell therapy

Advances in mass spectrometry, genome sequencing techniques, and bioinformatic strategies have accelerated the discovery of cancer-specific neoantigens. Tumors express multiple immunogenic neoantigens, and neoantigen-specific T cell receptors (TCRs) can be identified in peripheral blood's mononuclear cells in cancer patients. Therefore, individualized TCR-based therapies are a promising approach whereby multiple neoantigen-specific TCRs can be selected in each patient, potentially leading to a highly effective treatment for cancer patients. We developed three multiplex analytical assays to determine the quality attributes of the TCR-T cell drug product with a mixture of five engineered TCRs. The identity of each TCR was determined by two NGS-based methods, Illumina MiSeq and PacBio platforms. This approach not only confirms the expected TCR sequences but also differentiates them by their variable regions. The five individual TCR and total TCR knock-in efficiencies were measured by droplet digital PCR using specific reverse primers. A potency assay based on transfection of antigen-encoding-RNA was developed to assess the dose-dependent activation of T cells for each TCR by measuring the surface activation marker CD137 expression and cytokine secretion. This work provides new assays to characterize individualized TCR-T cell products and insights into quality attributes for the control strategy.

Neoepitopes prediction strategies: an integration of cancer genomics and immunoinformatics approaches

A major near-term medical impact of the genomic technology revolution will be the elucidation of mechanisms of cancer pathogenesis, leading to improvements in the diagnosis of cancer and the selection of cancer treatment. Next-generation sequencing technologies have accelerated the characterization of a tumor, leading to the comprehensive discovery of all the major alterations in a given cancer genome, followed by the translation of this information using computational and immunoinformatics approaches to cancer diagnostics and therapeutic efforts. In the current article, we review various components of cancer immunoinformatics applied to a series of fields of cancer research, including computational tools for cancer mutation detection, cancer mutation and immunological databases, and computational vaccinology.

Neoantigens: promising targets for cancer therapy

Recent advances in neoantigen research have accelerated the development and regulatory approval of tumor immunotherapies, including cancer vaccines, adoptive cell therapy and antibody-based therapies, especially for solid tumors. Neoantigens are newly formed antigens generated by tumor cells as a result of various tumor-specific alterations, such as genomic mutation, dysregulated RNA splicing, disordered post-translational modification, and integrated viral open reading frames. Neoantigens are recognized as non-self and trigger an immune response that is not subject to central and peripheral tolerance. The quick identification and prediction of tumor-specific neoantigens have been made possible by the advanced development of next-generation sequencing and bioinformatic technologies. Compared to tumor-associated antigens, the highly immunogenic and tumor-specific neoantigens provide emerging targets for personalized cancer immunotherapies, and serve as prospective predictors for tumor survival prognosis and immune checkpoint blockade responses. The development of cancer therapies will be aided by understanding the mechanism underlying neoantigen-induced anti-tumor immune response and by streamlining the process of neoantigen-based immunotherapies. This review provides an overview on the identification and characterization of neoantigens and outlines the clinical applications of prospective immunotherapeutic strategies based on neoantigens. We also explore their current status, inherent challenges, and clinical translation potential.

de la Fuente I. A Universal Antigen-Ranking Method to Design Personalized Vaccines Targeting Neoantigens against Melanoma

Background: The main purpose of this article is to introduce a universal mathematics-aided vaccine design method against malignant melanoma based on neoantigens. The universal method can be adapted to the mutanome of each patient so that a specific candidate vaccine can be tailored for the corresponding patient. Methods: We extracted the 1134 most frequent mutations in melanoma, and we associated each of them to a vector with 10 components estimated with different bioinformatics tools, for which we found an aggregated value according to a set of weights, and then we ordered them in decreasing order of the scores. Results: We prepared a universal table of the most frequent mutations in melanoma ordered in decreasing order of viability to be used as candidate vaccines, so that the selection of a set of appropriate peptides for each particular patient can be easily and quickly implemented according to their specific mutanome and transcription profile. Conclusions: We have shown that the techniques that are commonly used for the design of personalized anti-tumor vaccines against malignant melanoma can be adapted for the design of universal rankings of neoantigens that originate personalized vaccines when the mutanome and transcription profile of specific patients is considered, with the consequent savings in time and money, shortening the design and production time.

Boosting Antitumor Immunity with an Expanded Neoepitope Landscape

Immune-checkpoint blockade therapy has been successfully applied to many cancers, particularly tumors that harbor a high mutational burden and consequently express a high abundance of neoantigens. However, novel approaches are needed to improve the efficacy of immunotherapy for treating tumors that lack a high load of classic genetically derived neoantigens. Recent discoveries of broad classes of nongenetically encoded and inducible neoepitopes open up new avenues for therapeutic development to enhance sensitivity to immunotherapies. In this review, we discuss recent work on neoantigen discovery, with an emphasis on novel classes of noncanonical neoepitopes.

The Identification and Clinical Applications of Mutated Antigens in the Era of Immunotherapy

Simple Summary

In this review article, we highlight the process and importance of identifying neoantigens for immunotherapy for cancer. Although the process of identifying neoantigens may be time-consuming and costly, various efforts are being conducted at different stages of pre-clinical and clinical development to improve the process and identify neoantigens. This initiative will be imperative in developing the next generation of cost-efficient and potent immunotherapies against cancer.

Abstract

The era of personalized cancer therapy is here. Advances in the field of immunotherapy have paved the way for the development of individualized neoantigen-based therapies that can translate into favorable treatment outcomes and fewer side effects for patients. Addressing challenges related to the identification, access, and clinical application of neoantigens is critical to accelerating the development of individualized immunotherapy for cancer patients.

Neoantigens in precision cancer immunotherapy: from identification to clinical applications

Immunotherapies targeting cancer neoantigens are safe, effective, and precise. Neoantigens can be identified mainly by genomic techniques such as next-generation sequencing and high-throughput single-cell sequencing; proteomic techniques such as mass spectrometry; and bioinformatics tools based on high-throughput sequencing data, mass spectrometry data, and biological databases. Neoantigen-related therapies are widely used in clinical practice and include neoantigen vaccines, neoantigen-specific CD8+ and CD4+ T cells, and neoantigen-pulsed dendritic cells. In addition, neoantigens can be used as biomarkers to assess immunotherapy response, resistance, and prognosis. Therapies based on neoantigens are an important and promising branch of cancer immunotherapy. Unremitting efforts are needed to unravel the comprehensive role of neoantigens in anti-tumor immunity and to extend their clinical application. This review aimed to summarize the progress in neoantigen research and to discuss its opportunities and challenges in precision cancer immunotherapy.

dbPepNeo2.0: A Database for Human Tumor Neoantigen Peptides From Mass Spectrometry and TCR Recognition

Neoantigens are widely reported to induce T-cell response and lead to tumor regression, indicating a promising potential to immunotherapy. Previously, we constructed an open-access database, i.e., dbPepNeo, providing a systematic resource for human tumor neoantigens to storage and query. In order to expand data volume and application scope, we updated dbPepNeo to version 2.0 (http://www.biostatistics.online/dbPepNeo2). Here, we provide about 801 high-confidence (HC) neoantigens (increased by 170%) and 842,289 low-confidence (LC) HLA immunopeptidomes (increased by 107%). Notably, 55 class II HC neoantigens and 630 neoantigen-reactive T-cell receptor-β (TCRβ) sequences were firstly included. Besides, two new analytical tools are developed, DeepCNN-Ineo and BLASTdb. DeepCNN-Ineo predicts the immunogenicity of class I neoantigens, and BLASTdb performs local alignments to look for sequence similarities in dbPepNeo2.0. Meanwhile, the web features and interface have been greatly improved and enhanced.

The Importance of Being Presented: Target Validation by Immunopeptidomics for Epitope-Specific Immunotherapies

Presentation of tumor-specific or tumor-associated peptides by HLA class I molecules to CD8⁺ T cells is the foundation of epitope-centric cancer immunotherapies. While often in silico HLA binding predictions or in vitro immunogenicity assays are utilized to select candidates, mass spectrometry-based immunopeptidomics is currently the only method providing a direct proof of actual cell surface presentation. Despite much progress in the last decade, identification of such HLA-presented peptides remains challenging. Here we review typical workflows and current developments in the field of immunopeptidomics, highlight the challenges which remain to be solved and emphasize the importance of direct target validation for clinical immunotherapy development.

Application of Organoids in Carcinogenesis Modeling and Tumor Vaccination

Organoids well recapitulate organ-specific functions from their tissue of origin and remain fundamental aspects of organogenesis. Organoids are widely applied in biomedical research, drug discovery, and regenerative medicine. There are various cultivated organoid systems induced by adult stem cells and pluripotent stem cells, or directly derived from primary tissues. Researchers have drawn inspiration by combination of organoid technology and tissue engineering to produce organoids with more physiological relevance and suitable for translational medicine. This review describes the value of applying organoids for tumorigenesis modeling and tumor vaccination. We summarize the application of organoids in tumor precision medicine. Extant challenges that need to be conquered to make this technology be more feasible and precise are discussed.

The contribution of uncharted RNA sequences to tumor identity in lung adenocarcinoma

The identity of cancer cells is defined by the interplay between genetic, epigenetic transcriptional and post-transcriptional variation. A lot of this variation is present in RNA-seq data and can be captured at once using reference-free, k-mer analysis. An important issue with k-mer analysis, however, is the difficulty of distinguishing signal from noise. Here, we use two independent lung adenocarcinoma datasets to identify all reproducible events at the k-mer level, in a tumor versus normal setting. We find reproducible events in many different locations (introns, intergenic, repeats) and forms (spliced, polyadenylated, chimeric etc.). We systematically analyze events that are ignored in conventional transcriptomics and assess their value as biomarkers and for tumor classification, survival prediction, neoantigen prediction and correlation with the immune microenvironment. We find that unannotated lincRNAs, novel splice variants, endogenous HERV, Line1 and Alu repeats and bacterial RNAs each contribute to different, important aspects of tumor identity. We argue that differential RNA-seq analysis of tumor/normal sample collections would benefit from this type k-mer analysis to cast a wider net on important cancer-related events. The code is available at https://github.com/Transipedia/dekupl-lung-cancer-inter-cohort.

A simple and general strategy for postsurgical personalized cancer vaccine therapy based on an injectable dynamic covalent hydrogel

Cancer vaccines artificially stimulate the immune system against cancer and are considered the most promising treatment of cancer. However, the current progress in vaccine research against cancer is still limited and slow, partially due to the difficulties in identifying and obtaining tumor-specific antigens. Considering surgery as the first choice for tumor treatment in most cases, the authors evaluated whether the resected tumor can be directly used as a source of tumor antigens for designing personalized cancer vaccines. Based on this idea, herein, the authors report a dynamic covalent hydrogel-based vaccine (DCHVax) for personalized postsurgical management of tumors. The study uses proteins extracted from the resected tumor as antigens, CpG as the adjuvant, and a multi-armed poly(ethylene glycol) (8-arm PEG)/oxidized dextran (ODEX) dynamically cross-linked hydrogel as the matrix. Subcutaneous injection of DCHVax recruits dendritic cells to the matrix in situ and elicits robust tumor-specific immune responses. Thus, it effectively inhibits the postoperative growth of the residual tumor in several murine tumor models. This simple and personalized method to develop cancer vaccines may be promising in developing clinically relevant strategies for postoperative cancer treatment.

Implications of Antigen Selection on T Cell-Based Immunotherapy

Many immunotherapies rely on CD8+ effector T cells to recognize and kill cognate tumor cells. These T cell-based immunotherapies include adoptive cell therapy, such as CAR T cells or transgenic TCR T cells, and anti-cancer vaccines which expand endogenous T cell populations. Tumor mutation burden and the choice of antigen are among the most important aspects of T cell-based immunotherapies. Here, we highlight various classes of cancer antigens, including self, neojunction-derived, human endogenous retrovirus (HERV)-derived, and somatic nucleotide variant (SNV)-derived antigens, and consider their utility in T cell-based immunotherapies. We further discuss the respective anti-tumor/anti-self-properties that influence both the degree of immunotolerance and potential off-target effects associated with each antigen class.

RNA cancer vaccines: developing mRNA nanovaccine with self-adjuvant property for cancer immunotherapy

Messenger RNA (mRNA)-based cancer vaccine has become a popular approach for developing personalized and effective antitumor immunotherapy. To achieve robust antitumor efficacy, mRNA-encoding tumor antigens needs to be efficiently delivered and translated in dendritic cells for efficient antigen presentation; meanwhile, the vaccine would have adjuvant effect by stimulating innate immune response to boost the full activation of adaptive immunity. Recently, we reported a minimalist nanovaccine by formulating tumor antigen-encoding mRNA with a lipid-like material named C1, which could efficiently deliver mRNA into dendritic cells with simultaneous Toll-like receptor 4 (TLR4) stimulation, together induced T cell activation. Importantly, C1 mRNA nanovaccine exhibited significant antitumor efficacy on several tumor mouse models. Here, we discuss the nanovector-facilitated mRNA delivery and translation in dendritic cells, the self-adjuvant property of nanovectors, the challenges of personalized tumor antigen selection, and the potential strategies for developing efficacious mRNA cancer vaccines targeting the immunosuppressive tumor microenvironment.

Personalized immunotherapy in cancer precision medicine

With the significant advances in cancer genomics using next-generation sequencing technologies, genomic and molecular profiling-based precision medicine is used as a part of routine clinical test for guiding and selecting the most appropriate treatments for individual cancer patients. Although many molecular-targeted therapies for a number of actionable genomic alterations have been developed, the clinical application of such information is still limited to a small proportion of cancer patients. In this review, we summarize the current status of personalized drug selection based on genomic and molecular profiling and highlight the challenges how we can further utilize the individual genomic information. Cancer immunotherapies, including immune checkpoint inhibitors, would be one of the potential approaches to apply the results of genomic sequencing most effectively. Highly cancer-specific antigens derived from somatic mutations, the so-called neoantigens, occurring in individual cancers have been in focus recently. Cancer immunotherapies, which target neoantigens, could lead to a precise treatment for cancer patients, despite the challenge in accurately predicting neoantigens that can induce cytotoxic T cells in individual patients. Precise prediction of neoantigens should accelerate the development of personalized immunotherapy including cancer vaccines and T-cell receptor-engineered T-cell therapy for a broader range of cancer patients.

[In silico specificity determination of neoantigen-reactive T-lymphocytes]

Effective personalized immunotherapies of the future will need to capture not only the peculiarities of the patient's tumor but also of his immune response to it. In this study, using results of in vitro high-throughput specificity assays, and combining comparative models of pMHCs and TCRs using molecular docking, we have constructed all-atom models for the putative complexes of all their possible pairwise TCR-pMHC combinations. For the models obtained we have calculated a dataset of physics-based scores and have trained binary classifiers that perform better compared to their solely sequence-based counterparts. These structure-based classifiers pinpoint the most prominent energetic terms and structural features characterizing the type of protein-protein interactions that underlies the immune recognition of tumors by T cells.

The abscopal effect: a sense of DNA damage is in the air

Tumor metastasis is a singularly important determinant of survival in most cancers. Historically, radiation therapy (RT) directed at a primary tumor mass was associated infrequently with remission of metastasis outside the field of irradiation. This away-from-target or "abscopal effect" received fringe attention because of its rarity. With the advent of immunotherapy, there are now increasing reports of abscopal effects upon RT in combination with immune checkpoint inhibition. This sparked investigation into underlying mechanisms and clinical trials aimed at enhancement of this effect. While these studies clearly attribute the abscopal effect to an antitumor immune response, the initial molecular triggers for its onset and specificity remain enigmatic. Here, we propose that DNA damage-induced inflammation coupled with neoantigen generation is essential during this intriguing phenomenon of systemic tumor regression and discuss the implications of this model for treatment aimed at triggering the abscopal effect in metastatic cancer.

Cancer Vaccines: Antigen Selection Strategy

Unlike traditional cancer therapies, cancer vaccines (CVs) harness a high specificity of the host’s immunity to kill tumor cells. CVs can train and bolster the patient’s immune system to recognize and eliminate malignant cells by enhancing immune cells’ identification of antigens expressed on cancer cells. Various features of antigens like immunogenicity and avidity influence the efficacy of CVs. Therefore, the choice and application of antigens play a critical role in establishing and developing CVs. Tumor-associated antigens (TAAs), a group of proteins expressed at elevated levels in tumor cells but lower levels in healthy normal cells, have been well-studied and developed in CVs. However, immunological tolerance, HLA restriction, and adverse events are major obstacles that threaten TAA-based CVs’ efficacy due to the “self-protein” characteristic of TAAs. As “abnormal proteins” that are completely absent from normal cells, tumor-specific antigens (TSAs) can trigger a robust immune response against tumor cells with high specificity and without going through central tolerance, contributing to cancer vaccine development feasibility. In this review, we focus on the unique features of TAAs and TSAs and their application in vaccines, summarizing their performance in preclinical and clinical trials.